Patent Grant
12156096
The following description relates to determining a location of motion detected from wireless signals based on wireless link counting.
Motion detection systems have been used to detect movement, for example, of objects in a room or an outdoor area. In some example motion detection systems, infrared or optical sensors are used to detect movement of objects in the sensor's field of view. Motion detection systems have been used in security systems, automated control systems and other types of systems.
In some aspects of what is described here, the location of motion in a space (e.g., the particular room in a house where a person is moving, a particular floor or quadrant of a building where a person is moving, etc.) may be detected using information from multiple wireless communication devices communicating with each other wirelessly.
For instance, wireless signals received at each of the wireless communication devices in a wireless communication network may be analyzed to determine channel information for the different communication links in the network (between respective pairs of wireless communication devices in the network). The channel information may be representative of a physical medium that applies a transfer function to wireless signals that traverse the space. In some instances, the channel information includes channel response information. Channel response information may refer to known channel properties of a communication link, and may describe how a wireless signal propagates from a transmitter to a receiver, representing the combined effect of, for example, scattering, fading, and power decay within the space between the transmitter and receiver. In some instances, the channel information includes beamforming state information. Beamforming (or spatial filtering) may refer to a signal processing technique used in multi antenna (multiple-input/multiple-output (MIMO)) radio systems for directional signal transmission or reception. Beamforming can be achieved by combining elements in an antenna array in such a way that signals at particular angles experience constructive interference while others experience destructive interference. Beamforming can be used at both the transmitting and receiving ends in order to achieve spatial selectivity. In some cases (e.g., the IEEE 802.11ac standard), a beamforming steering matrix is used by a transmitter. The beamforming steering matrix may include a mathematical description of how the antenna array should use each of its individual antenna elements to select a spatial path for transmission. While certain aspects are described herein with respect to channel response information, beamforming state information or beamformer steering matrix state may also be used in the aspects described as well.
The channel information for each of the communication links may be analyzed (e.g., by a hub device or other device in the network, or a remote device communicably coupled to the network) to detect whether motion has occurred in the space, to determine a relative location of the detected motion, or both. In some aspects, the channel information for each of the communication links may be analyzed to detect whether an object is present or absent, e.g., when no motion is detected in the space.
In some implementations, the wireless communication network may include a wireless mesh network. A wireless mesh network may refer to a decentralized wireless network whose nodes (e.g. wireless communication devices) communicate directly in a point-to-point manner without using a central access point, base station or network controller. Wireless mesh networks may include mesh clients, mesh routers, or mesh gateways. In some instances, a wireless mesh network is based on the IEEE 802.11s standard. In some instances, a wireless mesh network is based on Wi-Fi ad hoc or another standardized technology. Examples of commercially-available wireless mesh networks include Wi-Fi systems sold by Google, Eero, and others.
In some example wireless communication networks, each node is connected to one or more other nodes through one or more bi-directional links. Each node can analyze the wireless signals that it receives to identify the perturbation or disturbance on each of the links. The disturbance on each link can be represented as a motion indicator value, for example, as a scalar quantity that can be normalized. The link disturbance values from the nodes in the wireless communication network can be used to determine the probability of motion at the locations associated with the respective node. For example, the probability of motion at each node can be used to tell which node has the highest probability of having motion in its vicinity, and that node can be identified as the node around which the motion occurred. In order to do this, the analysis can be case in a Bayesian estimation framework, for the recursive computation of probabilities. The probabilistic framework offers a number of technical advantages, for example, providing recursive estimation and hence eventual convergence to a correct result, simplistic logic with no conditions for each special situation, performance that is more accurate and robust (e.g., to artifacts) and others.
In addition, physical insights regarding the motion detection system can inform the Bayesian estimation framework that is used to detect the location of motion. For example, the relative magnitude of excitation on a link (between a transmitter node and receiver node) is likely to be greater when the motion that creates the excitation is nearer the receiver node. Accordingly, as an initial probability estimate for where motion occurred, the highest probabilities can be assigned to the receiver nodes on wireless links associated with the highest motion indicator values. This initial probability estimate can be combined with a conditional probability distribution (e.g., based on prior motion data) to produce a recursively refined probability estimate according to a Bayesian framework. As another example, in certain contexts the likelihood of motion transitioning between distinct locations can be higher or lower, relative to the likelihood of motion remaining in a single location. Accordingly, location transition probabilities can be incorporated into the Bayesian framework. For example, a transition probability matrix can be combined with the initial probability estimate and the conditional probability distribution to produce the recursively refined probability estimate according to the Bayesian framework.
The example wireless communication devices 102A, 102B, 102C can operate in a wireless network, for example, according to a wireless network standard or another type of wireless communication protocol. For example, the wireless network may be configured to operate as a Wireless Local Area Network (WLAN), a Personal Area Network (PAN), a metropolitan area network (MAN), or another type of wireless network. Examples of WLANs include networks configured to operate according to one or more of the 802.11 family of standards developed by IEEE (e.g., Wi-Fi networks), and others. Examples of PANs include networks that operate according to short-range communication standards (e.g., BLUETOOTH®, Near Field Communication (NFC), ZigBee), millimeter wave communications, and others.
In some implementations, the wireless communication devices 102A, 102B, 102C may be configured to communicate in a cellular network, for example, according to a cellular network standard. Examples of cellular networks include networks configured according to 2G standards such as Global System for Mobile (GSM) and Enhanced Data rates for GSM Evolution (EDGE) or EGPRS; 3G standards such as Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Universal Mobile Telecommunications System (UMTS), and Time Division Synchronous Code Division Multiple Access (TD-SCDMA); 4G standards such as Long-Term Evolution (LTE) and LTE-Advanced (LTE-A); 5G standards, and others. In the example shown in
In some cases, the wireless communication devices 102A, 102B, 102C may be Wi-Fi access points or another type of wireless access point (WAP). The wireless communication devices 102A, 102B, 102C may be configured to perform one or more operations as described herein that are embedded as instructions (e.g., software or firmware) on the wireless communication devices. In some cases, one or more of the wireless communication devices 102A, 102B, 102C may be nodes of a wireless mesh network, such as, for example, a commercially-available mesh network system (e.g., Google Wi-Fi, Eero Wi-Fi systems, etc.). In some cases, another type of standard or conventional Wi-Fi transceiver device may be used. The wireless communication devices 102A, 102B, 102C may be implemented without Wi-Fi components; for example, other types of wireless protocols for wireless communication, either standard or non-standard, may be used for motion detection.
In the example shown in
The wireless communication devices transmitting motion probe signals, e.g. 102A, 102B, may be referred to as source devices. In some cases, wireless communication devices 102A, 102B may broadcast the wireless motion probe signals (e.g., described above). In other cases, the wireless communication devices 102A, 102B may send wireless signals addressed to another wireless communication device 102C and other devices (e.g., a user equipment, a client device, a server, etc.). The wireless communication device 102C as well as the other devices (not shown) may receive the wireless signals transmitted by the wireless communication devices 102A, 102B. In some cases, the wireless signals transmitted by the wireless communication devices 102A, 102B are repeated periodically, for example, according to a wireless communication standard or otherwise.
In some examples, the wireless communication device 102C, which may be referred to as a sensor device, processes the wireless signals received from the wireless communication devices 102A, 102B to detect motion, or lack of motion, of an object in a space accessed by the wireless signals. In some examples, another device or computing system processes the wireless signals received by the wireless communication device 102C from the wireless communication devices 102A, 102B to detect motion, or lack of motion, of an object in a space accessed by the wireless signals. In some cases, the wireless communication device 102C (or another system or device) processes the wireless signals to detect the presence or absence of an object in a space when lack of motion is detected. In some instances, the wireless communication device 102C (or another system or device) may perform one or more operations as described in relation to
The wireless signals used for motion and/or presence detection can include, for example, a beacon signal (e.g., Bluetooth Beacons, Wi-Fi Beacons, other wireless beacon signals), pilot signals (e.g., pilot signals used for channel sounding, such as in beamforming applications, according to the IEEE 802.11ac-2013 standard), or another standard signal generated for other purposes according to a wireless network standard, or non-standard signals (e.g., random signals, reference signals, etc.) generated for motion and/or presence detection or other purposes. In some cases, the wireless signals for motion and/or presence detection are known to all devices in the network.
In some examples, the wireless signals may propagate through an object (e.g., a wall) before or after interacting with a moving object, which may allow the moving object's movement to be detected without an optical line-of-sight between the moving object and the transmission or receiving hardware. In some cases, the wireless signals, when received by a wireless communication device, e.g. 102C, may indicate lack of motion in a space, for example, that an object is not moving, or no longer moving, in the space. In some cases, the wireless signals, when received by a wireless communication device, e.g. 102C, may indicate the presence of an object in the space when lack of motion is detected. Conversely, the wireless signals may indicate the absence of an object in the space when lack of motion is detected. For example, based on the received wireless signals, the third wireless communication device 102C may generate motion data, presence data, or both. In some instances, the third wireless communication device 102C may communicate the motion detection and/or presence data, to another device or system, such as a security system, that may include a control center for monitoring movement within a space, such as a room, building, outdoor area, etc.
In some implementations, the wireless communication devices 102A, 102B may be configured to transmit motion probe signals (e.g., as described above) on a wireless communication channel separate from wireless network traffic signals (e.g., a frequency channel or coded channel). For example, the modulation applied to the payload of a motion probe signal and the type of data or data structure in the payload may be known by the third wireless communication device 102C, which may reduce the amount of processing that the third wireless communication device 102C performs for motion and presence detection. The header may include additional information such as, for example, an indication of whether motion or lack of motion was detected by another device in the communication system 100, whether a presence of an object was detected by another device in the communication system 100, an indication of the modulation type, an identification of the device transmitting the signal, and so forth.
In the example shown in
In some instances, the motion detection zones 110 can include, for example, air, solid materials, liquids, or another medium through which wireless electromagnetic signals may propagate. In the example shown in
In some implementations, the wireless communication system 100 may be, or may include, a motion detection system. The motion detection system may include one or more of the wireless communication devices 102A, 102B, 102C and possibly other components. One or more wireless communication devices 102A, 102B, 102C in the motion detection system may be configured for motion detection, presence detection, or both. The motion detection system may include a database that stores signals. One of the wireless communication devices 102A, 102B, 102C of the motion detection system may operate as a central hub or server for processing received signals and other information to detect motion and/or presence. The storage of data—e.g., in the database, and/or the determination of motion, lack of motion (e.g., a steady state), or presence detection—may be performed by a wireless communication device 102, or in some cases, may be performed by another device in the wireless communication network or in the cloud (e.g., by one or more remote devices).
In some cases, a combination of one or more of the wireless communication devices 204A, 204B, 204C can form, or may be part of, a dedicated motion detection system. For example, as part of the dedicated motion detection system, one or more of the wireless communication devices 204A, 204B, 204C may be configured for motion detection, presence detection, or both, in the motion detection system. In some cases, a combination of one or more of the wireless communication devices 204A, 204B, 204C may be, or may be part of, an ad hoc motion detection system that also performs other types of functions.
The example wireless communication devices 204A, 204B, 204C may transmit and/or receive wireless signals through a space 200. The example space 200 may be completely or partially enclosed or open at one or more boundaries of the space 200. The space 200 may be or may include an interior of a room, multiple rooms, a building, an indoor area, outdoor area, or the like. A first wall 202A, a second wall 202B, and a third wall 202C at least partially enclose the space 200 in the example shown.
In the example shown in
As shown, an object is in a first position 214A at an initial time t=0 in
As shown in
In
The example wireless signals shown in
In the example shown in
As shown in
Mathematically, a transmitted signal f(t) transmitted from the first wireless communication device 204A may be described according to Equation (1):
where ωn represents the frequency of nth frequency component of the transmitted signal, cn represents the complex coefficient of the nth frequency component, and t represents time. With the transmitted signal f(t) being transmitted from the first wireless communication device 204A, an output signal rk(t) from a path k may be described according to Equation (2):
where αn,k represents an attenuation factor (or channel response; e.g., due to scattering, reflection, and path losses) for the nth frequency component along path k, and ϕn,k represents the phase of the signal for nth frequency component along path k. Then, the received signal R at a wireless communication device can be described as the summation of all output signals rk(t) from all paths to the wireless communication device, which is shown in Equation (3):
Substituting Equation (2) into Equation (3) renders the following Equation (4):
The received signal R at a wireless communication device can then be analyzed. The received signal R at a wireless communication device can be transformed to the frequency domain, for example, using a Fast Fourier Transform (FFT) or another type of algorithm. The transformed signal can represent the received signal R as a series of n complex values, one for each of the respective frequency components (at the n frequencies ωn). For a frequency component at frequency ωn, a complex value Yn may be represented as follows in Equation (5):
The complex value Yn for a given frequency component on indicates a relative magnitude and phase offset of the received signal at that frequency component on. When an object moves in the space, the complex value Yn changes due to the channel response αn,k of the space changing. Accordingly, a change detected in the channel response (and thus, the complex value Yn) can be indicative of movement of an object within the communication channel. Conversely, a stable channel response (or “steady state”), for example, when no change or only small changes are detected in the channel response (or the complex value Yn), indicates lack of movement. Thus, in some implementations, the complex value Yn for each of multiple devices in a wireless mesh network can be analyzed to detect whether motion has occurred, or whether there is lack of motion, in a space traversed by the transmitted signals f(t). In some cases, when lack of movement is detected, further analysis may be performed on the channel response to determine if an object is present in the space, but not moving.
In another aspect of
In some implementations, for example, a steering matrix may be generated at a transmitter device (beamformer) based on a feedback matrix provided by a receiver device (beamformee) based on channel sounding. Because the steering and feedback matrices are related to the propagation characteristics of a channel, these matrices change as objects move within the channel. Changes in the channel characteristics are accordingly reflected in these matrices, and by analyzing the matrices, motion can be detected, and different characteristics of the detected motion can be determined. In some implementations, a spatial map may be generated based on one or more beamforming matrices. The spatial map may indicate a general direction of an object in a space relative to a wireless communication device. In some cases, “modes” of a beamforming matrix (e.g., a feedback matrix or steering matrix) can be used to generate the spatial map. The spatial map may be used to detect the presence of motion in the space or to detect a location of the detected motion.
In some instances, the channel information (e.g., channel response information or beamforming state information, as described above) derived from wireless signals can be used to compute motion indicator values. For example, a set of motion indicator values for a given time frame may represent the levels of disturbance detected on the respective wireless links that communicated the wireless signals during the time frame. In some cases, the channel information can be filtered or otherwise modified, for instance, to reduce the effects of noise and interference on the motion indicator values. In some contexts, a higher magnitude motion indicator value may represent a higher level of disturbance, while a lower magnitude motion indicator value may represent a relatively lower level of disturbance. For instance, each motion indicator value can be an individual scalar quantity, and the motion indicator values can be normalized (e.g., to unity or otherwise).
In some cases, the motion indicator values associated with a time frame can be used collectively to make an overall determination, for example, whether motion occurred in the space during the time frame, where motion occurred in the space during the time frame, etc. For instance, a motion consensus value for a time frame may indicate the overall determination of whether motion occurred in the space based on all (or a subset) of motion indicator values for the time frame. In some cases, a more accurate, reliable or robust determination can be made by analyzing multiple motion indicator values for a time frame collectively. And in some cases, data sets can be updated recursively to further improve the accuracy, for example, of location determinations. For instance, the motion indicator values for each sequential time frame can be used to recursively update data sets representing the conditional probability of detecting motion at distinct locations in the space, and the recursively updated data sets can be used to make an overall determination of where motion occurred during a subsequent time frame.
The wireless communication network 300 includes wireless communication channels 304 communicatively coupling respective pairs of wireless nodes 302. Such communicative coupling may allow an exchange of wireless signals between wireless nodes 302 over a time frame. In particular, the wireless communication channels 304 allow bi-directional communication between the respective pairs of wireless nodes 302. Such communication may occur along two directions simultaneously (e.g., full duplex) or along only one direction at a time (e.g., half duplex). In some instances, such as shown in
Each wireless communication channel 304 includes two or more wireless links, including at least one for each direction in the bi-directional communication. In
In some implementations, the wireless communication network 300 obtains a set of motion indicator values associated with a time frame, which may include the processes of motion detection described in relation to
In some variations, the wireless communication network 300 may include a data processing apparatus that executes program instructions (e.g., a network server, a wireless communication device, a network router, etc.). The program instructions may cause the data processing apparatus to assign a unique node identifier to each of the wireless nodes 302 in the wireless communication network 300. The unique node identifier may be mapped to a media access control (MAC) address value, which corresponds to a MAC address (or portion thereof) associated with a wireless node. For example, the wireless nodes N0, N1, and N2 of
The MAC address values may be ordered, from left to right, to indicate respective pairs of transmitting and receiving wireless nodes in a wireless link. In particular, the left MAC address value may correspond to a transmitting wireless node and the right MAC address value may correspond to a receiving wireless node. Such mappings of unique node and link identifiers may aid the data processing apparatus in performing operations, such as searching, sorting, and matrix manipulation, during processes of motion detection.
The program instructions may additionally cause the data processing apparatus to poll the wireless links (or wireless nodes 302) to obtain motion indicator values for each wireless link in the plurality of wireless links. For example, the wireless links of the wireless communication network 300 of
In the data structure, the first column corresponds to the unique link identifiers of the wireless links and the second column of the data structure corresponds to their respective motion indicator values. The data structure may be an array, as shown above, or some other type of data structure (e.g., a vector). Although data structure is presented as having three significant digits for each motion indicator value, other numbers of significant digits are possible for the motion indicator values (e.g., 2, 5, 9, etc.).
Now referring to
The data processing apparatus executes program instructions to generate, from the data structure 402, wireless links that are present in the wireless communication network during the time frame. The generated wireless links and their respective motion indicator values may be stored in a first memory of the data processing apparatus (or motion detection system) that serves as a link dictionary. The link dictionary is shown by block 404 of
The data processing apparatus also executes program instructions to generate, from the data structure 402, wireless nodes present in the wireless communication network during the time frame. In particular, the program instructions direct the data processing apparatus to “split” each generated wireless link into individual wireless nodes of its respective pair of wireless nodes, as shown in block 406. The program instructions also direct the data processing apparatus to sort or filter through the individual wireless nodes to identify unique wireless nodes in the wireless communication network during the time frame. Given that a single wireless node may be shared in common between two or more wireless links, the link dictionary 404 alone may not be sufficient in establishing unique wireless nodes of the wireless communication network. The unique wireless nodes may then be stored in a second memory of the data processing apparatus (or motion detection system) that serves as a node dictionary. The node dictionary is shown by block 408 of
A node counter and persistence calculator may be communicatively-coupled to the node dictionary 408, as shown by block 410. In many instances, the node counter and persistence calculator 410 is part of the data processing apparatus. The node counter and persistence calculator 410 is operable to track wireless nodes present in the wireless communication network over successive time frames and update the node dictionary 408 accordingly. Such tracking may include timing an appearance (or disappearance) of one or more wireless nodes. For example, when a new wireless node connects to the wireless communication network, the node counter and persistence calculator 410 updates the node dictionary 408 to include the new wireless node. In another example, when a wireless node disconnects from the wireless communication network, the node counter and persistence calculator 410 updates the node dictionary 408 to remove the disconnected wireless node. Such updating may occur after a predetermined number of time frames have elapsed where the wireless node is not connected to the wireless communication network.
The data processing apparatus additionally executes program instructions to alter one or more magnitudes of the set of motion indicator values to reference each motion indicator value to a common scale of wireless link sensitivity. More specifically, the data processing apparatus may function, in part, as a link strength estimator, such as shown by block 412, and a link equalizer, such as shown by block 414. The link strength estimator 412 and the link equalizer 414 receive, from the link dictionary 404, an identity of wireless links that are present in the wireless communication network during the time frame as well as their respective motion indicator values. The link equalizer 414 also receives, from the link strength estimator 412, an equalization value for each of the identified wireless links. The link strength estimator 412 and the link equalizer 414 operate cooperatively to reference the motion indicator values of each identified wireless links to a common scale of wireless link sensitivity.
In operation, the link strength estimator 412 estimates a link strength of the identified wireless links by determining a statistical property of their respective motion indicator values. The statistical property may be a maximum motion indicator value, a deviation of a motion indicator value from a mean value, or a standard deviation. Other statistical properties are possible. In some instances, the link strength estimator 412 tracks the statistical properties of one or more respective motion indicator values over successive time frames. The statistical property may allow the link strength estimator 412 to gauge an excitation strength and corresponding dynamic range of a wireless link. Such gauging may account for a unique sensitivity of each identified wireless link. The link strength estimator 412 passes the determined statistical values to the link equalizer 414, which in turn, utilizes them as equalization values for respective motion indicator values. In particular, the link equalizer 414 divides the motion indicator value of each identified wireless link with its respective equalization value (or statistical property) to generate a normalized motion indicator value. In this manner, the link equalizer 414 “equalizes” the identified wireless links so that their respective responses to motion or other events may be compared independent of sensitivity.
For example, due to motion or another event, a first subset of wireless links may become strongly excited and exhibit correspondingly high dynamic ranges (or sensitivities). A second subset of wireless links may become weakly excited and exhibit correspondingly low dynamic ranges (or sensitivities) due to the same motion or event. Such excitations and corresponding dynamic ranges are reflected in the motion indicator values received by the link strength estimator 412 and the link equalizer 414 from the link dictionary 404. However, the link strength estimator 412 and link equalizer 414 operate cooperative to normalize the received motion indicator values to a common scale of wireless link sensitivity. Such normalization ensures that comparisons of the first and second subsets of wireless links within the plurality of wireless links do not overweight the first subset of wireless links relative to the second subset. Other benefits are normalization are possible.
The program instructions may further cause the data processing apparatus to identify a subset of wireless links based on a magnitude of their associated motion indicator values relative to the other motion indicator values in the set of motion indicator values. In particular, the data processing apparatus may receive the identified wireless links and their respective normalized motion indicator values from the link equalizer 414 and store this data in a memory associated with a likelihood calculator, such as shown by block 416. As part of this operation, the data processing apparatus may also receive the list of unique wireless nodes from the node dictionary 408 and store the list in the memory associated with the likelihood calculator 416. The data processing apparatus may function, in part, as the likelihood calculator 416.
The likelihood calculator 416 identifies a subset of wireless links based on a magnitude of their respective, normalized motion indicator values relative to other normalized motion indicator values. To do so, the likelihood calculator 416 may sort or filter through the normalized motion indicator values received from the link equalizer 414 to identify the subset of wireless links. For example, the likelihood calculator 416 may sort the data stored in the memory according to magnitude to determine a highest normalized motion indicator value, thereby generating a subset of wireless links with a single wireless link. In another example, the likelihood calculator 416 may sort the data in the memory according to magnitude to determine the three highest normalized motion indicator values, thereby generating a subset of wireless with three wireless links. Other numbers of wireless links are possible for the subset of wireless links.
The likelihood calculator 416 also generates count values for the wireless nodes connected to the wireless communication network during the time frame. The count value for each wireless node indicates how many wireless links in the subset of wireless links are defined by the wireless node. For example, and with reference to
The unique link identifiers of 3, 4, and 5 correspond to wireless nodes N0, N1, and N2 as shown below:
Here, wireless node N0 assists in defining one wireless link in the subset of wireless links, i.e., N2→N0. Similarly, wireless node N1 assists in defining two wireless links in the subset of wireless links, i.e., N1→N2 and N2→N1, and wireless node N2 assists in defining three wireless links in the subset of wireless links, i.e., N1→N2, N2→N0, and N2→N1. Accordingly, the likelihood calculator 416 generates count values of 1, 2, and 3 for respective wireless nodes N0, N1, and N2. In the present example, all wireless nodes of the wireless communication network assist in defining a wireless link of the subset of wireless links. However, for wireless nodes that do not assist in defining a wireless link of the subset of wireless links, the likelihood calculator 416 may generate a count value of zero. In some instances, the likelihood calculator 416 generates a count-value data structure associating each wireless node connected to the wireless communication network during the time frame with its respective count value. For the present example, the likelihood calculator 416 may generate the following the count-value data structure:
Although wireless nodes in the count-value data structure are represented by the label, Ni, where i represents a number of a wireless node, other representations are possible (e.g., partial MAC addresses).
The likelihood calculator 416 further generates a probability vector based on the count values that includes values for each wireless node connected to the wireless communication network during the time frame. The values for each connected wireless node represent a probability of motion at the connected wireless node during the time frame. In particular, the values may represent a probability that motion at (or proximate to) a respective wireless node induces link activity along a particular wireless link. In some instances, the values sum to unity. In these instances, the values may be probability values. The likelihood calculator 416 passes the generated probability vector to a Bayesian update engine, as shown in
In some instances, the values for each connected wireless node are likelihood values assigned from a link likelihood map. The likelihood values may not necessary sum to unity. The link likelihood map associates likelihood values with respective magnitudes of count values. The likelihood values and their associations may be predetermined and may further be stored in a memory of the likelihood calculator 416 (or data processing apparatus). For example, if a wireless node is strongly represented in a subset of wireless links, motion detected by the wireless communication network will have a relatively high probability of being located at or near the wireless node. As such, the link likelihood map may associate high likelihood values with proportionately high count values. However, other associations of likelihood values and count values are possible.
In some variations, the probability vector is represented by a probability vector, P(Lj|Ni), that includes probability values based on the link likelihood map. The probability values correspond to probabilities that a wireless link, Lj, exhibits link activity given motion at a wireless node, Ni. For example, and with reference to
Now referring back to
The probability mapper/redistributor 418 is also operable to generate an initialization probability vector of a plurality of initialization probability vectors 420 by altering values of the prior probability vector based on the change in wireless connectivity. For example, the change in wireless connectivity may include a wireless node that has disconnected from the wireless communication network between the prior and subsequent time frames. In this case, the probability mapper/redistributor 418 may generate the initialization probability vector by apportioning values of the prior probability vector associated with the disconnected wireless node to values of wireless nodes that have remained connected to the wireless communication network. Such apportioning may occur in ratios defined by the values of the remaining wireless nodes. However, other apportioning schedules are possible. In another example, the change in wireless connectivity may include a wireless node that has connected from the wireless communication network between the prior and subsequent time frames. In this case, the probability mapper/redistributor 418 generate the initialization probability vector by adding a value to the prior probability vector for the newly-connected wireless node.
The probability mapper/redistributor 418 may be operable to generate other types of initialization probability vectors that correspond to reset states. For example, if the wireless communication network (or motion detection system) is cold-started, the probability mapper/redistributor 418 may generate an initialization probability vector by assigning equal probability values to all unique wireless nodes listed in the node dictionary 408. In another example, if the wireless communication network (or motion detection system) is warm-started, the probability mapper/redistributor 418 may generate an initialization probability vector based on probability values that correspond to a time frame when motion was last detected. In yet another example, if the wireless communication network (or motion detection system) is operational but later reset, the probability mapper/redistributor 418 may utilize the prior probability vector as the initialization probability vector. In yet another example, if a user notifies the wireless communication network (or motion detection system) that he/she is leaving a monitored residence (e.g., through a mobile software application), the probability mapper/redistributor 418 may generate an initialization probability vector with probability values biased towards wireless nodes at a point of entry (e.g., a front door).
The probability mapper/redistributor 418 passes the plurality of initialization probability vectors 420 to a multiplexor (or mux), which also receives the prior probability vector from a motion model. The data processing apparatus may function, in part, as the multiplexor, such as shown by block 422. The multiplexor 422 is operable to select the prior probability vector or one of the plurality of initialization probability vectors based on the set of motion indicator values, a configuration of the wireless communication network, or both. The selected probability vector is then passed to the Bayesian update engine, as shown in
In some implementations, the data processing apparatus uses the selected probability vector and a set of motion indicator values associated with a second subsequent time frame to identify a location associated with motion that occurred during the subsequent time frame. In particular, executes program instructions to generate, from a first probability vector received from the likelihood calculator 416 and a second probability vector received from the multiplexor 422, a third probability vector that includes third values for each wireless node. In particular, the Bayesian update engine generates the third probability vector, as shown by block 428. The third values of the third probability vector represent probabilities of motion at the respective wireless nodes during the time frame.
In some variations, the second probability vector is represented by a probability vector, P(Ni), that includes probability values (or second values) representing a probability of motion at a wireless node, Ni. The probability of motion at wireless node, Ni, for P(Ni) is independent of link activity along any of wireless links, Lj, and may also be independent of other factors. For example, and with reference to
In some variations, the third probability vector is represented by P(Ni|Lj), where Ni corresponds to the unique node identifier and Lj corresponds to the unique link identifier. The third probability vector, P(Ni|Lj), includes third values that represent a probability of motion at wireless node, Ni, given link activity along wireless link, Lj. For example, if Lj corresponds to wireless link 1 in the wireless communication network 300 of
The third probability vector, P(Ni|Lj), may be determined by the Bayesian update engine 428 according to Eq. (1):
where P(Lj|Ni) and P(Ni) are as described above for, respectively, the first probability vector from the likelihood calculator 416 and the second probability vector from the multiplexor 422. Eq. (1) may allow the wireless communication network 300 (or data processing apparatus) to determine the location of detected motion using Bayesian statistics. For example, if in the wireless communication network 300 of
Such calculation results in P(Ni|1)={0.476, 0.095, 0.429}, with the third values summing to unity, i.e., 0.476+0.095+0.429=1. P(Ni|1) may therefore represent a probability distribution normalized to unity. In P(Ni|1), P(0|1) corresponds to the largest of the third values, indicating that motion detected by the wireless communication network 300 along wireless link 1 has the highest probability of being located at (or proximate to) wireless node 0. Based on this value of P(0|1), the program instructions may cause the data processing apparatus to look up the MAC address value of wireless node 0, and when found, output the result (e.g., output 7f4440).
In some implementations, the data processing apparatus performs an iterative process for sequential time frames. For example, the data processing apparatus may repeat the operations, over multiple iterations for respective time frames, of obtaining the set of motion indicator values associated with a subsequent time frame, identifying the subset of wireless links based on a magnitude of their associated motion indicator values relative to other motion indicator values in the set of motion indicator values, generating the count values for the wireless nodes connected to the wireless communication network during the subsequent time frame, generating the first probability vector based on the count values and including values for the connected wireless nodes. In some implementations, the repeated operations include obtaining a set of motion indicator values associated with a prior time frame, generating a prior probability vector associated with the prior time frame, generating a second probability vector by selecting the prior probability vector or one of the plurality of initialization probability vectors to.
In some implementations, the repeated operations may include generating a third probability vector based on the first values of the first probability vector and the second values of the second probability vector; identifying a wireless communication device associated with the highest of the third values; and identifying, by operation of a data processing apparatus, a location associated with the identified wireless communication device as a location of the motion detected from the wireless signals exchanged during the subsequent time frame.
An output of the Bayesian update engine 428 may be fed into the motion model to generate the prior probability vector (or second probability vector), which is passed to the probability mapper/redistributor 418 and the multiplexor 422. The data processing apparatus may function, in part, as the motion model, as shown by block 430. The motion model 430 may operate analogous to calculating probabilities on a trellis.
In some implementations, the wireless communication network may include a network topology, a motion topology, or both. The network topology is defined by wireless links in the wireless communication network that are active in a time frame. Such activity may be represented by an exchange of data between two wireless nodes associated with an active wireless link in the time frame. As such, the network topology includes the wireless nodes associated with the active wireless links. By comparison, the motion topology is defined by active wireless links in the network topology that become excited by motion of an object in the time frame. The motion topology includes the wireless nodes associated with the excited wireless links and a connected state of those wireless nodes. The connected state is defined by a number of wireless nodes connected to a wireless node of interest, such as through respective wireless communication channels. Examples of the connected state include singly-connected, doubly-connected, triply-connected, and so forth.
For example,
The excited wireless links are part of wireless communication channels 804 between wireless node N0 and wireless nodes N1 and N2 and serve as a basis to define a motion topology. In particular, the motion topology of the example wireless communication network 800 is defined by wireless nodes N0, N1, and N2 and excited wireless links L01, L10, L02, and L20. The motion topology does not include wireless links L12 and L21, which are not excited by the motion of the object. Two wireless communication channels 804 are present in the motion topology, i.e., one extending between wireless nodes N0 and N1 and one extending between wireless nodes N1 and N2. Wireless node N0 is thus wirelessly connected to two other nodes and has a connected state that is doubly-connected. Similarly, wireless nodes N1 and N2 are each connected to one other node and have respective connected states that are singly-connected (in the motion topology). Although
In some variations, the wireless communication network 800 corresponds to a wireless mesh network and the wireless nodes N0, N1, and N2 correspond to access point (AP) nodes of the wireless mesh network. The access point nodes may generate motion-sensing data based on wireless signals transmitted between respective pairs of the access point nodes, such as along wireless links L01, L10, L02, L20, L12 and L21 of the wireless communication channels 804. The motion-sensing data may be generated in response to motion of an object in a space traversed by the wireless signals (e.g., as indicated by dashed arrow 806). However, the motion-sensing data may also be generated in the absence such motion.
Now referring back to
The data processing apparatus may function in part as a likelihood calculator, as shown in block 902. In this capacity, the data processing apparatus may generate a probability vector in response to receiving the list of unique wireless nodes and the list of identified wireless links. The probability vector is based on count values for each unique wireless node in the list of unique wireless nodes, as described previously in relation to the likelihood calculator 416 of
The data processing apparatus may additionally function in part as the node tagger, as shown in block 906. The node tagger 906 receives the list of excited wireless links from the link aggregator/filter 904, and for each excited wireless link in the list, determines the wireless nodes defining the excited wireless link. For each unique wireless node so-determined, the node tagger 906 generates a probability vector that includes a probability value for each possible connected state of the determined wireless node. Examples of possible connected states include singly-connected, doubly-connected, triply-connected, and so forth. The node tagger 906 then assigns a “tag” to each determined wireless node indicating the connected state of the determined wireless node during the time frame. In doing so, the node tagger 906 may generate, for the time frame, a list of determined wireless nodes and their respective “tags”. In many instances, the “tag” assigned to a determined wireless node corresponds to the connected state having the highest probability in the probability vector.
In some implementations, after the “tags” have been assigned, the node tagger 906 resolves a motion topology (or motion-sensing topology) of the wireless communication network for the time frame. To do so, the node tagger 906 may use the “tag” assigned to each determined wireless node and the network topology of the wireless communication network. The motion topology may be stored in a motion topology database that includes an identity of the excited wireless links, an identity of the determined wireless nodes, and the “tag” of each determined wireless node.
In some implementations, the node tagger 906 may repeat the operations of determining wireless nodes, generating probability vectors, and assigning “tags” to update the “tag” associated with each determined wireless node. Such repetition may allow the node tagger 906 to generate and maintain a “tag” database that includes all wireless nodes in the wireless communication network, their respective probability vectors, and their respective “tags”. The “tags” in the “tag” database may be updated as the probability vectors change, e.g., as subsets of wireless links are excited over successive time frames in response to motion of an object.
The node tagger 906 passes the list of the determined wireless nodes and their respective “tags” to an adaptive modulator, which is shown by block 908. The adaptive modulator 908 also receives a count-value data structure from the likelihood calculator 902. The count-value data structure associates each wireless node connected to the wireless communication network during the time frame with a count value. The count value indicates how many excited wireless links are defined by the wireless node. The adaptive modulator 908 then compares the count value of a determined wireless node with a threshold count value associated with the “tag” assigned to the wireless node. If the count value is less than the threshold count value, the wireless node is removed from the count-value data structure. The threshold count value may represent a minimum number of excited wireless links necessary to consider a connected state as participating in the detection of motion. The adaptive modulator 908 repeats this comparison for each determined wireless node to generate a modified count-value data structure. The modified count-value data structure is then passed back to the likelihood calculator 902, which uses the modified count-value data structure to generate probability vectors for a Bayesian update engine (e.g., the Bayesian update engine 428 of
In some implementations, the data processing apparatus executes program instructions to modify the count-value data structure based on a motion topology in a variable timeframe. The variable timeframe may be altered in duration to accommodate a length of time that motion takes to excite wireless links in a wireless communication network. For example, if the wireless nodes N1 and N2 of
The flowchart 1000 includes features analogous to those already described in relation to the flowchart 900 of
The adaptive lag link aggregator 1010 also determines a count value for each unique wireless node based on the list of excited wireless links. The count value indicates how many excited wireless links—including instances thereof—are defined by the unique wireless node. Using the count values, the adaptive lag link aggregator 1010 generates an adapted count-value data structure that associates each unique wireless node with a count value. The adaptive lag link aggregator 1010 passes the adapted count-value data structure to a link aggregator with lag/filter, as shown in block 1004. The adaptive lag link aggregator 1010 also passes the list of excited wireless links to a node tagger, as shown in block 1006. The link aggregator with lag/filter 1004 and the node tagger 1006 may operate analogously to the link aggregator/filter 904 and node tagger 906 described in relation to
In many implementations, the adaptive lag link aggregator 1010 builds a delay line. The delay line not only keeps track of the instantaneous excited link vector, but also a number (N) of last excited link vectors. The delay line allows the data processing apparatus to capture a pair of wireless links that are not triggered or excited together (e.g., due to a pathology) and enable them to be aggregated in the same time frame. Such aggregation allows wireless links belonging to one wireless node to appear in the same buffer while simultaneously pointing to the “true” motion topology of a wireless node. The aggregation may also assist the node tagger 1006 in accurately assigning “tags”. Depending on circumstance, the aggregation can occur using a number of different processes. For example, the adaptive lag link aggregator 1010 can determine all unique wireless links in the N element buffer, and then form an output based on all of the unique wireless links. In another example, the adaptive lag link aggregator 1010 can use the three most-often excited wireless links in the buffer. Other examples of process are possible. However, in general, the aggregation process can be employed to feed more nuanced versions of excited wireless links to the node tagger 1006.
The delay line may also be applicable for calculating a likelihood, e.g., to allow the motion to excite all relevant wireless links connected to a wireless node. The likelihood calculator 1002 also needs the frequency of wireless nodes appearing in the excited wireless links in order to compute the likelihood of motion being at the wireless node. This aggregation can be performed in the same way as described above. Alternatively, a different criterion may be selected, though fashioned along the same lines. In
The data processing apparatus may also function in part as a topological inconsistency calculator, as shown in block 1012. The topological inconsistency calculator 1012 tracks a duration of the variable timeframe over time to remove inconsistencies between the motion and network topologies of the wireless communication network. In particular, the topological inconsistency calculator 1012 compares the motion topology with the network topology during the variable timeframe to determine a connectivity difference. The motion topology is received from the node tagger 1006, and in some instances, includes a motion topology database. If the connectivity difference is greater than a threshold difference, the topological inconsistency calculator 1012 sends a signal to the adaptive lag link aggregator 1010 to increase the duration of the variable timeframe. The signal may specify an increment value, which is represented in
In many variations, the connectivity difference may be determined using a metric (e.g., a probability value) based on a connected state of a wireless nodes in the motion and network topologies. For example, the connectivity difference may be determined based on a probability of a wireless node being in a doubly-connected state in the motion topology versus a probability of the wireless node being in a doubly-connected state in the network topology. The latter value may be calculated from the observed link vector available at the input of the flowchart 1000. The observed link vector indicates which of the wireless links in the wireless communication network are reporting their motion indicators. If a wireless node appears in a doubly-connected state often in this reported vector—e.g., all four wireless links are connected to the wireless node are present all the time—then the wireless node has a high probability of being doubly-connected in the network topology. In contrast, the probability of the wireless node being doubly-connected in the motion topology is based on the excited wireless links, not the reported wireless links. If a wireless node is connected to all four wireless links, and all of those four wireless links get excited simultaneously and often, the wireless node has a high probability of being doubly-connected in the motion topology.
In some implementations, a method for determining a location of motion includes obtaining motion-sensing data from access point (AP) nodes of a wireless mesh network. The motion-sensing data is based on wireless signals transmitted between respective pairs of the AP nodes. In some instances, the motion-sensing data includes motion indicator values, which may be computed from channel information derived from the wireless signals. However, the motion-sensing data may include other types of data, such as a list of unique wireless nodes during a time frame and a list of wireless links identified as present in the wireless mesh network during the time frame.
The method also includes identifying a motion-sensing topology (or motion topology) of the wireless mesh network. The motion-sensing topology is identified based on tags assigned to respective AP nodes. Each tag indicates a connected state of a respective AP node. The method additionally includes generating a probability vector based on the motion-sensing data and the motion-sensing topology. The probability vector includes values that represent probabilities of motion of an object at respective AP nodes. In many instances, generation of the probability occurs by operation of the data processing apparatus, which executes program instructions corresponding to one or more of the flowcharts depicted in
In some implementations, generating the probability vector includes generating a count-value data structure based on the motion-sensing data. The count-value data structure includes count values for respective AP. Each count value indicates a number of wireless links defined by a respective AP node that were, according to the motion-sensing data, excited by motion. Generating the probability vector also includes modifying the count-value data structure based on the motion-sensing topology and generating the probability vector based on the modified count-value data structure. In further implementations, identifying the motion-sensing topology includes identifying, based on the motion-sensing data, the wireless links excited by motion and generating state probability vectors for the respective AP nodes. Each state probability vector includes values for connected states of a respective AP node, the value for each connected state representing a probability of the AP node being in the connected state. Identifying the motion-sensing topology also includes assigning the tags to the AP nodes based on the respective state probability vectors.
The values of the state probability vectors may include a first probability value and a second probability value. For example, the first probability value may represent a probability that an AP node is in a singly-connected state, and the second probability vector may represent a probability that the AP node is in a doubly-connected state. The values of the state probability vector may also include a third probability value representing a probability that the AP node is in a triply-connected state. Other probability values and corresponding connected states are possible. In many implementations, the tag assigned to each respective AP node indicates a connected state associated with a highest probability value in the state probability vector for the respective node.
In implementations where generating probability vector includes modifying the count-value data structure, such modification may include comparing the count value for a first AP node with a threshold count value and removing the first AP node from the count-value data structure if the count value is less than the threshold count value. The threshold count value is associated with the tag assigned to the first AP node and may represent a minimum number of excited wireless links necessary to consider a connected state as participating in the detection of motion.
In implementations where generating probability vector includes generating the count-value data structure, such generation may include generating a list of wireless links in the wireless mesh network that were, according to the motion-sensing data, excited by motion. The generation may also include generating a list of the AP nodes that includes an instance of an AP node for each instance, in the list of wireless links, a wireless link is defined by the AP node. The generation may additionally include setting the count value for the respective AP nodes equal to a number of times the respective AP node appears in the list of AP nodes.
In some implementations, identifying the motion-sensing topology includes identifying a difference between a first estimated motion-sensing topology and a network topology based on data collected over a first time duration. Identifying the motion-sensing topology also includes identifying a match between a second estimated motion-sensing topology and the network topology based on data collected over a second, longer time duration. The second estimated motion-sensing topology is then selected as the motion-sensing topology.
In some implementations, the wireless mesh network includes one or more leaf nodes in addition to the AP nodes. In these implementations, determining the location of the motion includes identifying one of the AP nodes or one of the leaf nodes as the location of the motion. In some implementations, the motion-sensing data is based on wireless signals transmitted between the respective pairs of the AP nodes during a first time frame, and the probability vector includes a first probability vector representing the first time frame. In such implementations, determining the location of the motion includes using a Bayesian calculator to determine the location of the motion based on the first probability vector and a prior probability vector representing a prior time frame.
The aforementioned methods and their variations may be implemented using a system that includes the wireless mesh network and its AP nodes (or leaf nodes, if present), one or more processors, and memory storing instructions that, when executed by the one or more processors, causes the system to perform operations of the methods and their variations. The aforementioned methods and their variations may also be stored as instructions on a non-transitory computer-readable medium. The instructions, when executed by a data processing apparatus, cause the data processing apparatus to perform the operations of the methods and their variations.
It will be appreciated that a wireless communication network may reconfigure its topology to improve network performance, such as to increase throughput or reduce latency between wireless nodes (or leaf nodes connected to the wireless nodes). Such reconfiguration may result in the wireless communication network having different topologies at different time frames. In some implementations, the data processing apparatus executes program instructions to determine a location of motion based on motion indicator values that are collected during different time frames, such as during a subsequent time frame and a prior time frame. The subsequent time frame and the prior time frame correspond to different topologies and may allow the data processing apparatus to determine the location of motion more accurately than if motion indicator values were used only from the subsequent time frame.
For example,
However, in the prior time frame, the example wireless communication network 1200 had a network topology in which wireless node N2 was doubly-connected, as shown in
In some variations, the wireless communication network 1200 corresponds to a wireless mesh network and the wireless nodes N0, N1, and N2 correspond to access point (AP) nodes of the wireless mesh network. The access point nodes may generate motion-sensing data based on wireless signals transmitted between respective pairs of the access point nodes, such as along wireless links L01, L10, L02, L20, L12 and L21. The motion-sensing data may be generated in response to motion of an object in a space traversed by the wireless signals (e.g., as indicated by dashed arrow 1206). However, the motion-sensing data may also be generated in the absence such motion.
The flowchart 1300 includes features analogous to those already described in relation to the flowchart 1000 of
The data processing apparatus may also function in part as the deep node tagger, as shown in block 1314. In response to the first signal, the deep node tagger 1314 samples motion indicator values from pairs of excited wireless links associated with the stable wireless node. Each pair of excited wireless links defines a wireless communication channel between the stable wireless node and another wireless node, and all such pairs are sampled. The deep node tagger 1314 generates a probability density function (PDF) for each pair of excited wireless links from its respective sampled motion indicator values. The deep node tagger 1314 then compiles a “deep tag” that includes an identity of the stable wireless node, an identity of a pair of excited wireless links, and a respective probability density function. The “deep tag” may be stored in a “deep tag” database as a recorded “deep tag”.
For example, and with reference to
After the topology of the wireless communication network has changed, such as in a subsequent time frame, the adaptive modulator 1308 may identify a wireless node as inconsistent. In particular, the adaptive modulator 1308 may determine that a motion topology associated with the wireless node is persistently inconsistent with a network topology within a variable timeframe. Such persistent inconsistency may result from the topological inconsistency calculator 1312 increasing a duration of a variable timeframe to a maximum duration. The adaptive modulator 1308 then identifies a pair of wireless links associated with the inconsistent wireless node that is being excited by motion. The adaptive modulator 1308 also queries the “deep tag” dataset to find a recorded “deep tag” that references the identified pair of wireless links and a second wireless node different from the inconsistent wireless node (i.e., the pair of wireless links communicatively couple the inconsistent wireless node and the second wireless node). The adaptive modulator 1308 then sends a second signal to the deep node tagger 1314 to sample motion indicator values from the identified pair of wireless links.
In response to the second signal, the deep node tagger 1314 samples motion indicator values from the identified pair of wireless links and generates a corresponding probability density function. The deep node tagger 1314 then creates a “deep tag” associating the inconsistent wireless node with the identified pair of wireless links and corresponding probability density function. This “deep tag” serves as a test “deep tag” that is passed back to the adaptive modulator 1308.
After receiving the test “deep tag” from deep node tagger 1314, the adaptive modulator 1308 determines a distance between the probability density function of the recorded “deep tag” and the probability density function of the test “deep tag”. For example, the adaptive modulator 1308 may compute a Kullback-Leibler divergence between the probability density functions to determine the distance. If the distance is less than or equal to a distance threshold, the adaptive modulator 1308 modifies a link likelihood map to include likelihood values biased towards the second wireless node. If the distance is greater than the distance threshold, the adaptive modulator 1308 modifies a link likelihood map to include likelihood values biased towards the inconsistent wireless node. The modified link likelihood map is then passed to the likelihood calculator 1302, which uses the modified link likelihood map to generate probability vectors for a Bayesian update engine (e.g., the Bayesian update engine 428 of
In some implementations, a method for determining a location of motion includes storing first motion-sensing statistics derived from first motion-sensing data associated with a first time frame. The first motion-sensing statistics may include a “deep tag” that references an identity of a stable wireless node, pairs of excited wireless links defined in part by the stable wireless node, and respective probability density functions. Other statistics are possible. The first motion-sensing data is based on wireless signals transmitted between respective pairs of access point (AP) nodes in a wireless mesh network during the first time frame while the wireless mesh network operates in a first motion-sensing topology (or first motion topology). In some instances, the first motion-sensing data includes motion indicator values, which may be computed from channel information derived from the wireless signals. However, the first motion-sensing data may include other types of data, such as a list of unique wireless nodes during the first time frame and a list of wireless links identified as present in the wireless mesh network during the first time frame.
The method also includes obtaining second motion-sensing data based on wireless signals transmitted between respective pairs of the AP nodes during a second, subsequent time frame while the wireless network operates in a second, distinct motion-sensing topology (or second motion topology). The second motion-sensing statistics may include a test “deep tag” that references an identity of an inconsistent wireless node, pairs of excited wireless links defined in part by the inconsistent wireless node, and respective probability density functions. Other statistics are possible. The second motion-sensing data is based on wireless signals transmitted between respective pairs of AP nodes in the wireless mesh network during the second, subsequent time frame while the wireless mesh network operates in the second motion-sensing topology (or second motion topology). In some instances, the second motion-sensing data includes motion indicator values, which may be computed from channel information derived from the wireless signals. However, the second motion-sensing data may include other types of data, such as a list of unique wireless nodes during the second time frame and a list of wireless links identified as present in the wireless mesh network during the second time frame.
In in response to detecting an inconsistency associated with the second motion-sensing topology—e.g., an inconsistency between the second motion-sensing topology and a network topology of the wireless mesh network—the method includes obtaining second motion-sensing statistics derived from the second motion-sensing data and comparing the first motion-sensing statistics with the second motion-sensing statistics. The method additionally includes generating a probability vector based on the comparison. The probability vector includes values that represent probabilities of motion of an object at respective AP nodes during the second time frame. In many instances, generation of the probability occurs by operation of the data processing apparatus, which executes program instructions corresponding to one or more of the flowcharts depicted in
In some implementations, the method includes identifying a set of wireless links in the wireless mesh network that were, according to the second motion-sensing data, excited by motion during the second time frame. The second motion-sensing statistics include test deep tags associated with the identified set of wireless links. Each test deep tag includes a respective test probability density function associated with the second time frame. The first motion-sensing statistics include reference deep tags associated with the identified set of wireless links. Each reference deep tag includes a respective reference probability density function associated with the first time frame. In these implementations, the method may include generating the test probability density functions based on sampling subsets of the first motion-sensing data and generating the reference probability density functions based on sampling subsets of the second motion-sensing data. In these implementations, the method may also include generating a count-value data structure that includes count values for respective AP nodes. Each count value indicates a number of wireless links defined by a respective AP node that were excited by motion during the second time period. The count-value data structure is then modified based on a comparison between the test deep tags and the reference deep tags, and the probability vector is generated based on the modified count-value data structure. In some instances, modifying the count-value data structure includes decreasing the count value for an AP node if a difference between the test deep tag and the reference deep tag is greater than a threshold difference. In some instances, modifying the count-value data structure includes increasing the count value for an AP node if a difference between the test deep tag and the reference deep tag is less than a threshold difference.
In some implementations, comparing the first motion-sensing statistics with the second motion-sensing statistics includes computing a Kullback-Leibler divergence between a test probability density function and a reference probability density function. In some implementations, the method includes identifying the first motion-sensing topology based on first tags assigned to respective AP nodes. Each of the first tags indicates a connected state of a respective AP node. In these implementations, the method also includes identifying the second motion-sensing topology based on second tags assigned to respective AP nodes. Each of the second tags indicates a connected state of a respective AP node.
In some implementations, detecting an inconsistency associated with the second motion-sensing topology includes detecting that the second-motion sensing topology does not match a network topology of the wireless mesh network during the second time frame. In some implementations, the wireless mesh network comprises one or more leaf nodes in addition to the AP nodes. In these implementations, determining the location of the motion includes identifying one of the AP nodes or one of the leaf nodes as the location of the motion.
The aforementioned methods and their variations may be implemented using a system that includes the wireless mesh network and its AP nodes (or leaf nodes, if present), one or more processors, and memory storing instructions that, when executed by the one or more processors, causes the system to perform operations of the methods and their variations. The aforementioned methods and their variations may also be stored as instructions on a non-transitory computer-readable medium. The instructions, when executed by a data processing apparatus, cause the data processing apparatus to perform the operations of the methods and their variations.
Now referring to
In situations where motion occurs near wireless node N1, as shown by dashed arrow 1408, wireless links L101 and L011 will be excited substantially over wireless links L102 and L012. This excitation is conformant with the singly-connected state of wireless node N1 in the network topology. However, in situations where motion occurs near wireless node N2, as shown by dashed arrow 1410, wireless links L10 and L011 and wireless links L102 and L012 will both be excited. This excitation implies a doubly-connected state for wireless node N2 that is not conformant to the singly-connected state of wireless node N2 in the network topology. Moreover, due to the excitation, the motion topology of the example wireless communication network 1400 has more doubly-connected wireless nodes than the network topology.
In some variations, the wireless communication network 1400 corresponds to a wireless mesh network and the wireless nodes N0, N1, and N2 correspond to access point (AP) nodes of the wireless mesh network. The access point nodes may generate motion-sensing data based on wireless signals transmitted between respective pairs of the access point nodes, such as along wireless links L01, L10, L02, L20, L12 and L21 of the wireless communication channels 1404, 1406. The motion-sensing data may be generated in response to motion of an object in a space traversed by the wireless signals (e.g., as indicated by dashed arrow 1408 or dashed arrow 1410). However, the motion-sensing data may also be generated in the absence such motion.
The flowchart 1500 includes features analogous to those already described in relation to the flowchart 1000 of
In parallel with the node tagger process path, the flowchart 1500 includes a second parallel process path. The second parallel process path is operable to distinguish between wireless nodes sharing a common connected state, such as the doubly-connected state of wireless nodes N0 and N2 in the motion topology shown by
In the present example, correlations within the correlation matrix come from two kinds of disturbances, a disturbance at wireless node N0 and a disturbance at node N2. These two disturbances get aggregated into the correlation matrix, and from a decomposition of the correlation matrix, two different flavors of the disturbances can be generated, e.g., two different orthogonal components of the correlation matrix. The decomposition of the correlation matrix may help determine two unique kinds of vectors (of link excitation data) that can explain all the observed variation whenever the four links are triggered. The variation is aggregated by summing together the link vectors when they have four excited elements in them, then decompose the correlation matrix to find individual components creating that variation, and then projecting against those components to determine which component is the observed excitation closest to, and then assigning a likelihood based on that component's properties. The flowchart 1500 shows the process being done.
In second parallel process path, a matrix correlator takes the vector of four link excitation data, takes an outer product of it (e.g., to create a matrix), and sums it with the previous sum of outer products to create a running average of the correlation matrix. Then the correlation matrix is decomposed. This decomposition is triggered by the topological inconsistency calculator which now has two outputs instead of one. Previously, the topological inconsistency calculator was only triggering when the number of motion-connected nodes was less than the network connected nodes. But now it is also triggering in the opposite direction, for example, when it finds the number of motion-doubly-connected nodes to be higher than the network-doubly-connected nodes. When the topological inconsistency calculator determines that such a discrepancy exists, it then triggers the need to separate these wireless nodes based on something other than the motion-triggered wireless link behavior. At that instance, it triggers the matrix decomposition of the correlation matrix to happen, which leads to the formation of eigen components (or eigen vectors) of the matrix. The eigen components are then assigned tags, based on their special properties. The purpose of the tags is to assign one eigen component (or eigen vector) to wireless node N0 and the other eigen component (or eigen vector) to wireless node N2. Now when a new link excitation vector arrives carrying motion excitation on all four links, the data processing apparatus executes program instructions project the new link excitation vector (by computing the dot product) against both the component vectors. The likelihood is assigned on the basis of which dot product wins. The winning product's associated eigen-component-node is assigned a higher likelihood, and the loosing product's eigen-component-node is assigned a lower likelihood.
The formula below shows the matrix formation and the decomposition and the node assignment to eigen vectors:
In the formula, the leftmost column presents the denomination of all the wireless links present in the example wireless communication network. When all these wireless links get triggered, the link filter operates to filter that signal and assign it a vector symbol x, which consists of four values as listed, i.e., x1, x2, x3, and x4. Such a vector on average may be composed of two components (over time). In some cases, wireless node N2 generates such a vector. In some cases, wireless node N0 generates such a vector. However, the properties of the vectors are slightly different. For example, when wireless node N2 is triggered, wireless links L02 and L20 are triggered more than wireless links L01 and L10. This is because only a component of wireless links L01 and L10 passes through wireless node N2. Most energy of that wireless link is passing through the other path which does not go through wireless node N2. Hence excitation at wireless node N2 does not create the same kind of perturbation as it creates in wireless links L02 and L20. This fact is shown by putting the +S symbols in the place of two more excited wireless links L02 and L20. The purpose is not to show that the components are equal, but to demonstrate that, on average, they will be higher, when the excitation is coming wireless node N2 (e.g., motion occurring near wireless node N2), as opposed to excitation coming from wireless node N0. In this way, the second parallel process path can distinguish how to assign the components.
The three right-most columns of the formula show the form and decomposition of the matrix, and x represents a column vector of link excitations. The column vector is multiplied by a transposed version of itself to create a matrix which is averaged, as shown by the expectation operator E(XXT). In some instances, the average value is subtracted from x before forming the outer product. Such subtraction may be necessary if x is a non-zero mean vector. Once matrix formation is done, a singular value decomposition (SVD) splits the matrix into its eigen-components, which can be extracted from the matrix U at its output. Each column of U contains one eigen-component. The first two columns of U are taken to look for two eigen-components. In the components, the data processing apparatus determines which link pair excitations are higher. If link pair excitations connected to wireless links L02 and L20 are higher, the vector is assigned to node wireless node N2. A similar process can be applied to make an assignment based on the components of wireless links L01 and L10.
In this example, wireless node N2 contends with wireless node N0. Motion near wireless node N1 will only excite two wireless links, hence wireless node N1 will be classified as having singly-connected state in the motion topology and will have no incompatibility with the network topology. However, wireless nodes N0 and N2 need arbitration. Hence, the wireless links associated with wireless node N1, i.e., wireless links L01 and L10, may be used to assign the eigen tags. Motion at wireless node N0 will excite both the multipath components of wireless links L01 and L10 (as opposed to only one of them being excited with motion near wireless node N2). Since both components are being excited, the magnitude of that excitation will be higher than the other two wireless link components in one of the eigen-components of the matrix. These higher valued L01 and L10 pair components should be assigned to node wireless node N0, as described above. Once eigen assignment has happened, we are now ready. Each incoming link vector, if satisfying the excitation condition is projected on eigen components and a likelihood assigned (according to the table determined by a designer) based on the result of that projection.
In some implementations, a method for determining a location of motion includes storing a set of eigenvectors derived from first motion-sensing data associated with a first time frame. The first motion-sensing data is based on wireless signals transmitted between access point (AP) nodes in a wireless mesh network during the first time frame while the wireless mesh network operates in a first motion-sensing topology (or first motion topology). Each of the eigenvectors in the set is assigned to a respective one of the AP nodes. In some instances, the first motion-sensing data includes motion indicator values, which may be computed from channel information derived from the wireless signals. However, the first motion-sensing data may include other types of data, such as wireless nodes sharing a common connected state.
The method also includes obtaining a motion vector based on wireless signals transmitted between the AP nodes during a second, subsequent time frame while the wireless mesh network operates in a second, distinct motion-sensing topology. The motion vector includes motion indicator values for respective wireless links between the AP nodes. In response to detecting an inconsistency associated with the second motion-sensing topology, the motion vector is compared with the respective eigenvectors. The method additionally includes generating a probability vector based on the comparison. The probability vector includes values that represent probabilities of motion of an object at respective AP nodes during the second time frame. In many instances, generation of the probability occurs by operation of a data processing apparatus, which executes program instructions corresponding to one or more of the flowcharts depicted in
In some implementations, the method includes repeating the operations, over multiple iterations for respective second time frames, of obtaining a motion vector, comparing the motion vector, generating a probability vector, and determining a location of motion.
In some implementations, the method includes identifying the first motion-sensing topology based on first tags assigned to respective AP nodes. Each of the first tags indicates a connected state of a respective AP node. The method also includes identifying the second motion-sensing topology based on second tags assigned to respective AP nodes. Each of the second tags indicates a connected state of a respective AP node. In some implementations, detecting an inconsistency associated with the second motion-sensing topology includes detecting that the second-motion sensing topology does not match a network topology of the wireless mesh network during the second time frame. For example, detecting the inconsistency may include identifying a number of doubly-connected nodes in the motion-sensing topology that is greater than a number of doubly-connected nodes in the network topology.
In some implementations, the method includes generating the set of eigenvectors by at least obtaining an aggregate correlation matrix for the first time period based on the first motion-sensing data and performing a matrix decomposition of the aggregate correlation matrix to obtain the eigenvectors. In these implementations, the method may optionally include computing the aggregate correlation matrix by at least: [1] obtaining first motion vectors based on respective subsets of the first motion-sensing data, [2] for each of the subsets of the first motion-sensing data, multiplying the first motion vector by a transposed instance of the first motion vector to generate a correlation matrix, and [3] combining the correlation matrices for the subsets to obtain the aggregate correlation matrix. The first motion vectors each include first motion indicator values for respective wireless links between the AP nodes.
In some implementations, comparing the motion vector with the respective eigenvectors includes computing dot products between the motion vector and the respective eigenvectors. In these implementations, the probability vector is generated based on the dot products. In further implementations, the method includes generating a count-value data structure based on the motion-sensing data. The count-value data structure includes count values for respective AP nodes. Each count value indicates a number of wireless links defined by a respective AP node that were, according to the motion-sensing data, excited by motion. The method also includes modifying the count-value data structure based on the dot products and generating the probability vector based on the modified count-value data structure. In some variations, the dot products include first and second dot products in which the first dot product is computed from the respective eigenvector that is associated with a first AP node and the second dot product is computed from the respective eigenvector that is associated with a second AP node. The first dot product is higher than the second dot product. Moreover, modifying the count-value value data structure includes increasing the count value for the first AP node and decreasing the count value for the second AP node.
In some implementations, the wireless mesh network includes one or more leaf nodes in addition to the AP nodes. In these implementations, determining the location of the motion includes identifying one of the AP nodes or one of the leaf nodes as the location of the motion.
The aforementioned methods and their variations may be implemented using a system that includes the wireless mesh network and its AP nodes (or leaf nodes, if present), one or more processors, and memory storing instructions that, when executed by the one or more processors, causes the system to perform operations of the methods and their variations. The aforementioned methods and their variations may also be stored as instructions on a non-transitory computer-readable medium. The instructions, when executed by a data processing apparatus, cause the data processing apparatus to perform the operations of the methods and their variations.
Now referring to
In some variations, the wireless communication network 1600 corresponds to a wireless mesh network and the wireless nodes N0, N1, and N2 correspond to access point (AP) nodes of the wireless mesh network. The access point nodes may generate motion-sensing data based on wireless signals transmitted along the wireless communication channels 1604, 1608. For example, the motion-sensing data may be based on wireless signals transmitted between respective pairs of the access point nodes along wireless links L01, L10, L02, L20, L12 and L21. The motion-sensing data may also be based on wireless signals transmitted from a leaf node to an access point node along wireless links defining the wireless communication channels 1608. The motion-sensing data may be generated in response to motion of an object in a space traversed by the wireless signals (e.g., as indicated by dashed arrow 1610 or dashed arrow 1612). However, the motion-sensing data may also be generated in the absence such motion.
This proportion—which is also shown in
If two leaf-node wireless links are simultaneously triggered due to the close proximity of the leaf nodes, the likelihood function can be further modified to account for this scenario. In particular, the motion magnitude of the leaf nodes can be ordered. The highest magnitude can be given a rank or 1, the next highest magnitude 2, and so forth. Then, a multiplier variable (a) can be selected that is a number less than one, and specifies how much weight should be given to the motion rank information in order to determine a likelihood. The value of the multiplier variable may be selected to have a small value with a range from 0.01 to 0.09. The multiplier variable serves to amplify or de-amplify the motion rank information in the computation of the likelihood. For a leaf node showing the highest rank, the likelihood expression above would be modified by multiplication with the term (1−α×rank). If the rank is one, then α would be subtracted from one to create a relatively large weight. If the rank is 2, then 2α would be subtracted from one to create a smaller weight for that leaf node's likelihood. In this manner, a likelihood is assigned to all leaf nodes whose respective wireless links have shown motion excitation on them. Such assignment allows the motion intensity (or channel disturbance) present on each leaf-node wireless link to be accounted for in the likelihood of motion, and eventually in the probability of motion at that particular node.
In some implementations, a method for determining a location of motion includes obtaining motion-sensing data based on wireless signals exchanged on wireless links in a wireless mesh network comprising a plurality of nodes. The plurality of nodes includes a first access point (AP) node, one or more other AP nodes, and leaf nodes. The wireless links includes wireless links between the first AP node and the one or more other AP nodes as well as wireless links between the first AP node and a first subset of the leaf nodes. The method also includes identifying, based on the motion-sensing data, the first AP node as an estimated location of motion of an object.
In response to the first AP node being identified as the estimated location of motion, the method additionally includes generating a likelihood data structure including likelihood values assigned to respective nodes of the plurality of nodes. The likelihood values are assigned to the first subset of the leaf nodes and the first AP node being higher than the likelihood values assigned to other nodes in the wireless mesh network. In some variations, the likelihood values assigned to the first subset of the leaf nodes and the first AP node are at least an order of magnitude higher than the likelihood values assigned to other nodes in the wireless mesh network. A location of the motion of the object is determined based on the likelihood data structure. In some implementations, the method includes repeating the operations, over multiple iterations for respective time frames, of obtaining motion-sensing data, identifying a first AP node, generating a likelihood data structure, and determining a location of the motion.
In some implementations, determining the location of the motion of the object includes generating a probability vector based on the likelihood data structure. The probability vector includes probability values that represent probabilities of motion at respective nodes of the wireless mesh network. Determining the location of the motion of the object also includes selecting, based on the probability vector, the first AP node or one of the first subset of leaf nodes as the location of the motion.
In some implementations, the method includes computing the likelihood value assigned to the first AP node based on a ratio of a number of leaf nodes in the first subset and a number representing a connected state of the first AP node. In further implementations, the method includes computing the likelihood values assigned to the first subset of leaf nodes based on ranking motion indicator values associated with the wireless links between the first AP node and the first subset of leaf nodes. In some instances, the likelihood value assigned to each respective leaf node represents the product of a rank assigned to the leaf node multiplied by an amplification factor. In these instances, the method may optionally include assigning a uniform value to the other nodes in the wireless mesh network.
The aforementioned methods and their variations may be implemented using a system that includes the wireless mesh network and its AP and leaf nodes, one or more processors, and memory storing instructions that, when executed by the one or more processors, causes the system to perform operations of the methods and their variations. The aforementioned methods and their variations may also be stored as instructions on a non-transitory computer-readable medium. The instructions, when executed by a data processing apparatus, cause the data processing apparatus to perform the operations of the methods and their variations.
Some of the subject matter and operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Some of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on a computer storage medium for execution by, or to control the operation of, data-processing apparatus. A computer storage medium can be, or can be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. Moreover, while a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially generated propagated signal. The computer storage medium can also be, or be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices).
Some of the operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources.
The term “data-processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing. The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them.
A computer program (also known as a program, program instructions, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
Some of the processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random-access memory or both. Elements of a computer can include a processor that performs actions in accordance with instructions, and one or more memory devices that store the instructions and data. A computer may also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic disks, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a phone, an electronic appliance, a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive). Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices (e.g., EPROM, EEPROM, flash memory devices, and others), magnetic disks (e.g., internal hard disks, removable disks, and others), magneto optical disks, and CD ROM and DVD-ROM disks. In some cases, the processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
To provide for interaction with a user, operations can be implemented on a computer having a display device (e.g., a monitor, or another type of display device) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse, a trackball, a tablet, a touch sensitive screen, or another type of pointing device) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.
A computer system may include a single computing device, or multiple computers that operate in proximity or generally remote from each other and typically interact through a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), a network comprising a satellite link, and peer-to-peer networks (e.g., ad hoc peer-to-peer networks). A relationship of client and server may arise by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
While this specification contains many details, these should not be understood as limitations on the scope of what may be claimed, but rather as descriptions of features specific to particular examples. Certain features that are described in this specification or shown in the drawings in the context of separate implementations can also be combined. Conversely, various features that are described or shown in the context of a single implementation can also be implemented in multiple embodiments separately or in any suitable sub-combination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single product or packaged into multiple products.
A number of embodiments have been described. Nevertheless, it will be understood that various modifications can be made. Accordingly, other embodiments are within the scope of the following claims.
This application is a continuation of U.S. patent application Ser. No. 16/867,066, filed May 5, 2020, entitled “Detecting a Location of Motion Using Wireless Signals that Propagate Along Two or More Paths of a Wireless Communication Channel,” which claims priority to U.S. Provisional Application No. 62/908,401 filed Sep. 30, 2019 and entitled “Detecting a Location of Motion Using Wireless Signals and Topologies of Wireless Connectivity.” The priority application is hereby incorporated, in its entirety, by reference.
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| Number | Date | Country | |
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| Child | 17329621 | US |
